scholarly journals Towards Big Data: Digitising Economic and Business History

2020 ◽  
pp. 45-67
Author(s):  
Jari Eloranta ◽  
Pasi Nevalainen ◽  
Jari Ojala
Keyword(s):  
Big Data ◽  
Data Gathering ◽  
Business History ◽  
General State ◽  
Research Projects ◽  
Digital History ◽  
History Data ◽  
Digital Era ◽  
Research Outcomes ◽  
History Of

This chapter describes the experiences in computational and digital history of economic and business historians who for decades have been forerunners in digital history data gathering and computational analysis. It attempts to discuss the major developments within this area internationally and, in some specific cases, in Finland in the fields of digital economic and business history. It concentrates on a number of research projects that the authors have previously been involved in, as well as research outcomes by other economic and business historians within Finland and elsewhere. It is not claimed that the projects discussed are unique or ahead of their time in the field of economic and business history—on the contrary they are representing a more general state of the art within the field and used as illustrative cases illuminating the possibilities and challenges facing historians in the digital era.

scholarly journals Big Questions and Big Data: A Reply from the Collaboratory

2017 ◽  
Vol 62 (1) ◽  
pp. 123-130
Author(s):  
Karin Hofmeester ◽  
Christine Moll-Murata
Keyword(s):  
Big Data ◽  
Social Inequality ◽  
Economic History ◽  
Collective Agency ◽  
History Data ◽  
Labour Relations ◽  
Working Together ◽  
History Of

AbstractIn our reply to Elise van Nederveen Meerkerk’s “Big Questions and Big Data: The Role of Labour and Labour Relations in Recent Global Economic History”, we focus on her observations on the Global Collaboratory on the History of Labour Relations. We endorse many of her suggestions to connect global labour and economic history and to regard labour relations not only as a dependent variable. In fact, as the examples from various Collab workshops and publications show, some of these ideas are already being put into practice. These examples also show that if we seriously want to combine global labour and economic history data and join the debate on the growth (or decrease) in social inequality, workers’ individual and collective agency must be taken on board. Finally, we argue that global labour and economic historians can benefit most from each other’s disciplines by truly working together in collaborative projects, developing new theories, perhaps less grand than those with which economic historians attract so much attention, but more profound.

Teaching Piano Pedagogy

2019 ◽  
Author(s):  
Courtney Crappell
Keyword(s):  
Piano Pedagogy ◽  
Research Projects ◽  
Teaching Experiences ◽  
Course Materials ◽  
Piano Students ◽  
Piano Teachers ◽  
History Of ◽  
Easy Integration

For teachers of piano pedagogy, this book provides tools to transform college piano students into professional piano teachers. It is not simply a book about teaching piano—instead, it is a book about how piano students learn to teach. It helps teachers develop pedagogy course curricula, design and facilitate practicum-teaching experiences, and guide research projects in piano pedagogy. After an introduction to the history of the domain, to its related topics, and to course materials, the book gives unique perspectives on how pedagogy teachers can introduce students to course concepts and then how to help them put those concepts into practice. To facilitate easy integration into the curriculum, it provides example classroom exercises and assignments throughout the text, which are designed to help students understand and practice the related topics and skills.

Intersectionality as a Framework for Theory and Research in Feminist Psychology

2018 ◽  
Author(s):  
Leah R. Warner ◽  
Stephanie A. Shields
Keyword(s):  
Social Activism ◽  
Data Gathering ◽  
Psychological Research ◽  
Social Identities ◽  
Future Directions ◽  
Science Practices ◽  
Intersectionality Theory ◽  
Psychological Science ◽  
Psychological Theories ◽  
History Of

Intersectionality theory concerns the interdependence of systems of inequality and implications for psychological research. Social identities cannot be studied independently of one another nor separately from the societal processes that maintain inequality. In this chapter we provide a brief overview of the history of intersectionality theory and then address how intersectionality theory challenges the way psychological theories typically conceive of the person, as well as the methods of data gathering and analysis customarily used by many psychologists. We specifically address two concerns often expressed by feminist researchers. First, how to reconcile the use of an intersectionality framework with currently-valued psychological science practices. Second, how intersectionality transforms psychology’s concern with individual experience by shifting the focus to the individual’s position within sociostructural frameworks and their social and political underpinnings. In a concluding section we identify two future directions for intersectionality theory: how psychological research on intersectionality can facilitate social activism, and current developments in intersectionality theory.

ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

2021 ◽  
Vol 13 (2) ◽  
pp. 1-27
Author(s):  
A. Khalemsky ◽  
R. Gelbard
Keyword(s):  
Big Data ◽  
Visual Presentation ◽  
Trend Detection ◽  
Dynamic Visualization ◽  
Segmentation Process ◽  
Key Points ◽  
Wide Range ◽  
History Of ◽  
The Individual ◽  
Over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.

History of Post-Soviet Migrations in Russian Science of the 21st Century

Istoriya ◽  
2021 ◽  
Vol 12 (11 (109)) ◽  
pp. 0
Author(s):  
Efim Pivovar
Keyword(s):  
21St Century ◽  
Migration Policy ◽  
Important Research ◽  
Research Projects ◽  
Russian Science ◽  
History Of ◽  
Soviet Space ◽  
Cis Countries ◽  
Post Soviet Space

The paper is devoted to the latest Russian historiography of migration processes in the post-Soviet space. The author considers the most important research projects of academic institutions and universities of Russia in the field of history and modern dynamics of post-Soviet migrations, covers key trends and results of the development of migration issues in the framework of various areas of Russian science. The author comes to the conclusion about the need for further in-depth development of the recent history and modern trends in the migration policy of the CIS countries, the role of migration in the dialogue of cultures and civilizations in the post-Soviet space, including within the framework of international cooperation of Eurasian scientists.

The Convergence of IoT, Machine Learning, and Big Data for Advancing Flood Analytics Knowledge  

2021 ◽  
Author(s):  
Vidya Samadi ◽  
Rakshit Pally
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Language Processing ◽  
Situational Awareness ◽  
Critical Infrastructure ◽  
Probability Distributions ◽  
Data Gathering ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Crowd Intelligence

<p>Floods are among the most destructive natural hazard that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and agriculture. Internet of Things (IoTs), machine learning (ML), and Big Data are exceptionally valuable tools for collecting the catastrophic readiness and countless actionable data. The aim of this presentation is to introduce Flood Analytics Information System (FAIS) as a data gathering and analytics system.  FAIS application is smartly designed to integrate crowd intelligence, ML, and natural language processing of tweets to provide warning with the aim to improve flood situational awareness and risk assessment. FAIS has been Beta tested during major hurricane events in US where successive storms made extensive damage and disruption. The prototype successfully identifies a dynamic set of at-risk locations/communities using the USGS river gauge height readings and geotagged tweets intersected with watershed boundary. The list of prioritized locations can be updated, as the river monitoring system and condition change over time (typically every 15 minutes).  The prototype also performs flood frequency analysis (FFA) using various probability distributions with the associated uncertainty estimation to assist engineers in designing safe structures. This presentation will discuss about the FAIS functionalities and real-time implementation of the prototype across south and southeast USA. This research is funded by the US National Science Foundation (NSF).</p>

Collaborative Research Funding

2014 ◽  
Vol 36 (4) ◽  
Author(s):  
Markus Behnke ◽  
Laura McConnell ◽  
Chris Ober
Keyword(s):  
International Collaboration ◽  
Research Funding ◽  
International Committee ◽  
Research Projects ◽  
Scientific Success ◽  
Funding Sources ◽  
Chemistry Research ◽  
National Organizations ◽  
Recent Developments ◽  
History Of

Within a changing research world, international collaboration has become even more important in achieving scientific success. Given the increased need and desire for multinational research, the actors are forced to identify appropriate funding sources. Whereas, science knows no international boundaries, support for scientific research, including in chemical sciences, is mostly provided by the national funding organizations. This is particularly true for the chemical sciences, where most research projects are relatively small in size and with respect to the number of involved PIs. Traditionally, national organizations are reluctant to provide funds to non-domestic researchers, and in practice, funding truly international research projects can be a real challenge for a variety of technical and bureaucratic reasons. In an effort to change this, an international Committee on Chemistry Research Funding (CCRF)—backed by several leading funding organizations—was established by IUPAC in December 2007 to promote increased international collaboration and networking in the global chemistry community. The following report gives a short overview on the history of IUPAC’s involvement in service for chemistry research funding and on the most recent developments.

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